Detection and classification of groundnut leaf nutrient level extraction in RGB images. (January 2023) - Janani, M.; Jebakumar, R.
Highlights: In agriculture, identifying the deficient nutrient or excess nutrient in the leaf is a significant concern for attaining the higher yield and productivity of any crop. Various image processing techniques are developed for detecting and reducing the deficiency in the leaf. These detection mechanisms may lead to an irreversible stage of a crop while it fails in detecting the nutrient level of a leaf at an earlier period. When complexity increases in predicting the nutrient in a leaf will affect the quality and production of a crop. To overcome these issues, an intelligent system needs to be furnished for detecting the nutrient range in a leaf before any visible or tangible symptoms occurred. Abstract: In agriculture, identifying the deficient or excess nutrients in the leaf is a significant concern for attaining the higher yield and productivity of any crop. Several image processing techniques have been developed by various researchers for detecting and reducing the deficiency in plant leaves. Some of those detection mechanisms are not scalable, expensive, and may lead to an irreversible stage of a crop while they fail to detect the nutrient level of a leaf at an earlier period. When complexity increases during the prediction process, it leads to untimely predictions that will affect the quality and production of a crop. The Nutrient Range Analysis Based on Greenness (NRAG) system is proposed to detect and classify nitrogen nutrient of groundnut leaves. Median filter is selected for its highest peak signal to noise ratio (PSNR) value to reduce the noise occurrence from the collected groundnut dataset during pre-processing. Greenness percentage of groundnut leaves are calculated with colour features and found that it is directly proportional to nitrogen (N) nutrient. Then those features are given to the developed Convolution Neural Network (CNN) based Holding Vector Network (HVN) model, which helps to detect and classify the three different ranges of nutrients, including low N nutrient, no N deficiency, and excess N nutrient. The obtained results shows that the developed CNN-based HVN model achieved 95% training accuracy and 92% validation accuracy. The developed model can be adopted by all types of farmers in real time to predict nitrogen range because of its high accuracy, scalability, and cost effectiveness.